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A novel decision support system for the appraisal and selection of green warehouses

Author

Listed:
  • Sandra, Michael
  • Narayanamoorthy, Samayan
  • Ferrara, Massimiliano
  • Innab, Nisreen
  • Ahmadian, Ali
  • Kang, Daekook

Abstract

Global warming is a prevalent issue across the world. In supply chains, warehouses contribute significantly to the increase in greenhouse gas emissions. Achieving carbon reduction within the warehouse is an issue that must be addressed in the design of a green warehouse. The objective of this research is to establish a hybrid fuzzy multi-criteria decision-making (F-MCDM) paradigm for assisting stakeholders within a supply chain to select a sustainable and green warehouse. A case study is presented to determine the essential criteria for choosing a prospective green warehouse for storing dairy products. Based on the decision-maker’s opinion, a total of six criteria are taken from the literature. Besides the criterion “cost”, all the other five criteria focused on reducing the environmental footprint. “Bio polyurethane flooring”, “polyvinylidene fluoride walls” and “electric forklift” ranked the top three green warehouse selection criteria. The sensitivity study also confirms this analysis and the system’s stability. Additionally, the comparative analysis with the existing MCDM models justifies the superiority, reliability, and feasibility of the proposed technique. Moreover, out of the four green warehouse alternatives, warehouse number two got the highest rank. This research thus proves that sustainable building materials and energy-efficient technologies can successfully reduce the environmental footprint caused by warehouses.

Suggested Citation

  • Sandra, Michael & Narayanamoorthy, Samayan & Ferrara, Massimiliano & Innab, Nisreen & Ahmadian, Ali & Kang, Daekook, 2024. "A novel decision support system for the appraisal and selection of green warehouses," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:soceps:v:91:y:2024:i:c:s003801212300294x
    DOI: 10.1016/j.seps.2023.101782
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